Low-energy laser shock peening residual stress field prediction method and device

Abstract

The invention discloses a low-energy laser shock peening residual stress field prediction method and device. The method comprises the following steps: generating simulation data of low-energy laser shock peening based on process parameters and residual stress measurement results in test data; establishing a residual stress prediction model according to the simulation data; predicting a residual stress field of the workpiece subjected to low-energy laser shock peening by using the residual stress prediction model; according to the method, the simulation data is generated through the test data, and the residual stress prediction model corresponding to the test data is established in combination with the simulation data, so that the residual stress of the workpiece subjected to low-energy laser shock peening can be predicted through the residual stress simulation model, and the use of a simulation model occupying a large number of computing resources is avoided; the calculation amount is reduced, and the prediction efficiency is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of laser-assisted processing, and in particular relates to a method and a device for predicting residual stress fields of low-energy laser shock strengthening. Background Art

[0002] Residual stress has a significant impact on the performance and service life of materials. On the one hand, a reasonable residual stress distribution can improve the fatigue strength and wear resistance of the material; on the other hand, excessive residual stress may cause process defects such as warping, distortion, and cracking of the material, reduce the mechanical properties and corrosion resistance of the material, and even shorten the service life of the parts.

[0003] As one of the surface strengthening processes for high-temperature alloy compressor blades, low-energy laser shock peening can ensure good surface roughness while introducing residual compressive stress. Therefore, accurate prediction of residual stress is a research hotspot in the manufacture of high-temperature alloy blades. The existing residual stress prediction method is based on the finite element simulation model. However, the model requires a lot of calculations when simulating, which consumes a lot of computing resources. Summary of the invention

[0004] The purpose of the present invention is to provide a method and device for predicting the residual stress field of low-energy laser shock strengthening, which breaks through the large-scale simulation of low-energy laser shock strengthening and greatly reduces the use of computing resources.

[0005] The present invention adopts the following technical solution: a method for predicting residual stress field of low-energy laser shock strengthening, comprising the following steps:

[0006] Generate low-energy laser shock processing simulation data based on process parameters and residual stress measurement results in experimental data;

[0007] Establish a residual stress prediction model based on simulation data;

[0008] The residual stress field of the workpiece subjected to low-energy laser shock processing is predicted using the residual stress prediction model.

[0009] Furthermore, the simulation data for low energy laser shock peening is generated including:

[0010] Calibrate the FEM simulation model based on process parameters and residual stress measurement results from the test data;

[0011] The calibrated FEM simulation model is used to generate simulation data for low-energy laser shock processing.

[0012] Furthermore, the residual stress prediction model is:

[0013]

[0014] Among them, σ x (z) represents the predicted residual stress along the cutting direction at a depth of z on the workpiece, x represents the cutting direction, It represents the stress caused by the inherent strain at depth z on the workpiece along the cutting direction. It represents the restraint reaction force along the cutting direction at the depth z on the workpiece. It represents the stress generated along the cutting direction by the restraint reaction moment M at a depth of z on the workpiece.

[0015] Furthermore, the method for establishing the residual stress prediction model includes:

[0016] Stress calculation formula caused by design inherent strain

[0017] in, represents the stress caused by the inherent strain at depth z on the workpiece along the cutting direction, v represents the Poisson's ratio of the workpiece material, E is the Young's modulus of the workpiece material, ε x (z) represents the inherent strain along the cutting direction at a depth of z on the workpiece.

[0018] Furthermore, the calculation method of the inherent strain along the cutting direction at a depth of z on the workpiece is:

[0019]

[0020] Where A represents the coefficient of the inherent strain distribution area of ​​the workpiece, h s The coefficient representing the inherent strain layer depth of the workpiece, z d Indicates the depth corresponding to the maximum inherent strain of the workpiece.

[0021] Furthermore, the method for establishing the residual stress prediction model includes:

[0022] The stress calculation formula of the design constraint reaction moment M

[0023] in, It represents the stress generated by the restraint reaction moment M along the cutting direction at the depth z on the workpiece, N represents the coefficient of the stress generated by the restraint reaction moment M, and h d is the thickness of the workpiece.

[0024] Furthermore, the residual stress prediction model is established according to the simulation data, including:

[0025] With simulation data as input, the support vector machine regression method is used to calculate the relationship between the undetermined parameters in the residual stress prediction model and the low-energy laser shock processing parameters;

[0026] Among them, the parameters to be determined include the coefficient A of the inherent strain distribution area of ​​the workpiece, the coefficient h of the inherent strain layer depth s , the depth z corresponding to the maximum inherent strain of the workpiece d , and the coefficient N of the stress generated by the restraint reaction moment M, is a constant and is not affected by depth.

[0027] Furthermore, a residual stress prediction model is determined according to the relationship between the unknown parameters and the low-energy laser shock peening process parameters.

[0028] Another technical solution of the present invention: a low-energy laser shock strengthening residual stress field prediction device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the above method is implemented when the processor executes the computer program.

[0029] The beneficial effect of the present invention is that the present invention generates simulation data through test data, and then establishes a residual stress prediction model corresponding to the test data in combination with the simulation data, so that the residual stress of the workpiece subjected to low-energy laser shock strengthening can be predicted through the residual stress simulation model, avoiding the use of simulation models that occupy a large amount of computing resources, reducing the amount of calculation, and improving the prediction efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a schematic diagram of a method for predicting residual stress field of low-energy laser shock strengthening according to an embodiment of the present invention;

[0031] Figure 2 is a graph showing residual stress and strain results at different depths in an embodiment of the present invention;

[0032] Figure 3 Schematic diagram of residual stress field prediction error under different process parameters of GH4169D in an embodiment of the present invention;

[0033] Figure 4 Schematic diagram of residual stress field prediction error of GH4169G and GH4169 under different process parameters in an embodiment of the present invention;

[0034] Figure 5 Schematic diagram of residual stress field prediction error of TC17 under different process parameters in an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0036] As one of the surface strengthening processes for high-temperature alloy compressor blades, low-energy laser shock peening can ensure good surface roughness while introducing residual compressive stress. Therefore, accurate prediction of residual stress is a research hotspot in the manufacture of high-temperature alloy blades.

[0037] Based on the inherent strain theory and finite element method, this paper proposes a new residual stress prediction strategy for low-energy laser shock strengthening. It breaks through the large-scale simulation of low-energy laser shock strengthening, deeply explores the influence mechanism of process parameters on residual stress distribution, and reverses the phenomenon that the coupling effect of surface tensile strain and compressive strain leads to the spoon-shaped distribution of residual stress.

[0038] Residual stress tests were conducted on the same series of high-temperature alloys (GH4169, GH4169G, GH4169D) and TC17 titanium alloy. It was found that the residual stress prediction model for GH4169D is also applicable to GH4169G and GH4169, with an accuracy of more than 80%. Finally, a residual stress prediction model for TC17 was established according to the method proposed in the present invention, with an accuracy of more than 85%, proving the universality of the residual stress prediction strategy.

[0039] In the present invention, low-energy laser shock peening refers to laser shock peening with a laser energy of less than or equal to 1J.

[0040] Currently, simulation software is used to predict the residual stress length of laser shock hardening. However, due to the limitations of current hardware computing power, most simulation prediction processes require a lot of time (from a few hours to a few days, or even more than ten or dozens of days), which affects the implementation of the low-energy shock hardening process.

[0041] Therefore, the present invention discloses a method for predicting residual stress field of low-energy laser shock strengthening, such as Figure 1 As shown, the method includes the following steps: generating simulation data of low-energy laser shock strengthening based on process parameters and residual stress measurement results in experimental data; establishing a residual stress prediction model according to the simulation data; and predicting the residual stress field of a workpiece subjected to low-energy laser shock strengthening using the residual stress prediction model.

[0042] The present invention generates simulation data through test data, and then establishes a residual stress prediction model corresponding to the test data in combination with the simulation data, so that the residual stress of the workpiece subjected to low-energy laser shock strengthening can be predicted through the residual stress simulation model, thereby avoiding the use of simulation models that occupy a large amount of computing resources, reducing the amount of calculation, and improving the prediction efficiency. For the prediction process that the simulation software needs to run for several days, the method of the present invention can be completed in only tens of minutes.

[0043] That is to say, in the present invention, first, the finite element simulation parameters are calibrated by test data. Then, the residual stress field under different process parameters is calculated using the calibrated simulation model, and the residual stress of all nodes in the impact area is extracted. The support vector product regression method in the Pytho mathematical model library is used to calculate the unknown coefficients (A, h s , z d , K, N). Finally, the correlation model between process parameters and unknown coefficients was established by surface fitting method.

[0044] In one embodiment, generating low energy laser shock peening simulation data includes: calibrating the FEM simulation model based on process parameters and residual stress measurement results in test data; and generating low energy laser shock peening simulation data using the calibrated FEM simulation model.

[0045] Specifically, during the low-energy laser shock strengthening process, the water confinement layer prevents the high-temperature ablation of the workpiece surface by the laser and isolates the contact between the plasma and the metal surface, so the thermal effect is ignored; since the shock wave presents a Gaussian distribution on the time scale, the pressure wave simulated by the finite element is approximated as three straight lines, and the shock wave action time t 1 Set to 3τ (τ is the laser pulse width).

[0046] Calculate peak pressure using Fabbro's formula:

[0047]

[0048] Among them, P max is the peak pressure of low-energy laser shock strengthening, a represents the coefficient of internal energy conversion into thermal energy, is a constant, Z is the synthetic acoustic impedance, I 0 The power density for low-energy laser shock peening.

[0049] Power density I 0 The calculation formula is:

[0050]

[0051] Where, e is the laser energy and d is the spot diameter.

[0052] The calculation formula of the synthetic acoustic impedance Z is:

[0053]

[0054] Among them, Z GH4169D and Z water are the workpiece material acoustic impedance and the constrained layer acoustic impedance respectively.

[0055] The peak pressure of the low-energy laser shock strengthening is used as a parameter input into the FEM simulation model to provide operating parameters for the FEM simulation model.

[0056] The present invention sets the side of the workpiece model as a fixed constraint. At the same time, in order to avoid the influence of excessive stress gradient on the residual stress calculation, the distance between the impact area and the workpiece boundary is determined by simulation to be at least 1 / 3 of the workpiece side length (the workpiece is a square); at the same time, since the residual stress of laser shock strengthening is symmetrical, by setting a symmetrical boundary, the impact effect of 100 light spots is simulated using 1 / 4 of the model area.

[0057] Specifically, the present invention adopts a Gaussian distribution function to describe the inherent strain, that is, the calculation method of the inherent strain along the cutting direction when the depth on the workpiece is z is:

[0058]

[0059] Among them, ε x (z) represents the inherent strain along the cutting direction when the workpiece depth is z, ε x (z) is a curve, z represents the depth of the test point from the workpiece surface. A is the coefficient describing the inherent strain distribution area, h s is the coefficient that characterizes the depth of the inherent strain layer, z d is the depth corresponding to the maximum inherent strain of the workpiece. These three parameters are parameters that need to be obtained through later fitting.

[0060] Specifically, in the depth z direction of the workpiece, the stress caused by the inherent strain at the depth z can be calculated by the following formula:

[0061]

[0062] in, It represents the stress caused by the inherent strain along the cutting direction when the depth is z on the workpiece, E is the Young's modulus of the workpiece material, and v is the Poisson's ratio of the workpiece material.

[0063] Specifically, the stress generated by the constraint reaction force F is uniformly distributed along the z direction, and its value is a fixed constant K, as shown in the following formula:

[0064]

[0065] in, It represents the restraint reaction force along the cutting direction when the workpiece depth is z. It is only related to the material, so the value is a fixed constant.

[0066] Specifically, the stress generated by the constraint reaction moment M is symmetric about the mid-plane of the element and can be described by a linear function with a slope of N, as shown in the equation:

[0067]

[0068] in, It represents the stress generated by the restraint reaction moment M along the cutting direction at the depth z on the workpiece, N represents the coefficient of the stress generated by the restraint reaction moment M, and h d is the thickness of the workpiece.

[0069] Specifically, according to the stress superposition principle of material mechanics, the final low-energy laser shock strengthening residual stress prediction model is shown in the equation:

[0070]

[0071] Among them, σ x (z) represents the residual stress at depth z of the workpiece after low-energy laser shock strengthening.

[0072] In the specific implementation process, the test is first carried out to calibrate the FEM simulation model: the test sample size is 50mm×50mm×2mm, which is obtained by wire cutting from milled blanks. The test sample has initial residual stress, and the residual stress influence layer is within 200μm. In order to eliminate the initial residual stress, the test sample is pretreated by manual grinding and polishing. After polishing, the surface residual stress is measured by X-ray diffraction. When the surface residual stress is in the range of [-20MPa, +20MPa], the pretreatment is completed.

[0073] The laser generated by the laser generator acts on the workpiece surface, and the spot diameter is changed by adjusting the focus position. The water circulation system forms a water confinement layer with a thickness of 1mm on the workpiece surface.

[0074] Next, the residual stress field at different depths of the same point was measured. The sample was electropolished, and the residual stress of the depth profile was measured using an X-ray residual stress measurement system. The depth of each electropolished layer was about 50 μm. The obtained test data was used to calibrate the FEM simulation model.

[0075] The energy of low-energy laser shock peening propagates and reflects inside the material, making it difficult for the FEM calculation results to converge. Therefore, it is necessary to set a global damping factor f to reduce the shock wave oscillation time. f was calibrated based on the experimental results. First, the residual stress of the sample after laser shock peening was measured. Then, f was adjusted until the residual stress calculated by FEM was consistent with the experimental results, with a relative error of less than 10%. Through the above steps, f = 5.2 × 10 5 .

[0076] When d=1mm, r=50%, e=0.8J, Z water =1.48×10 6 kg / m 2 ·s,ZGH4169D =46.02×10 6 kg / m 2 ·s, the residual stress field of GH4169D laser shock hardening was calculated by FEM, and the material parameters are shown in Table 1. In order to illustrate the accuracy of the method of the present invention, the FEM prediction results are compared with the results of the formula calculation, where the formula stress curve and strain curve are calculated by the formula of the method of the present invention, and the FEM calculated stress is obtained by the FEM simulation model. The comparison results are shown in Table 1. Figure 2 As shown, r is the laser shock overlap ratio.

[0077] Then, the support vector machine regression method is used to calculate the relationship between the undetermined parameters in the residual stress prediction model and the low-energy laser shock strengthening process parameters using the simulation data as input; the undetermined parameters include the coefficient A of the inherent strain distribution area of ​​the workpiece, the coefficient h of the inherent strain layer depth, and the coefficient h of the residual stress prediction model. s , the depth z corresponding to the maximum inherent strain of the workpiece d , σ x F (z) and the coefficient N of the stress generated by the restraint reaction moment M, σ x F (z) is a constant and is not affected by depth.

[0078] That is to say, all the FEM calculation data are used as training sets, and the optimal regression parameters are obtained by SVR method, and 5 parameters are obtained, namely A = 2.95, h s =0.71, z d =0.47, K = 104.62, N = -97.55. The goodness of fit of the residual stress prediction model is 96.8%.

[0079] Table 1 Linear elastic parameters of GH4169D, GH4169G, GH4169, TC17

[0080]

[0081] Table 2 shows the process parameters in the low-energy laser shock peening test (the first row of parameters of GH4169D is used in this embodiment). Among these parameters, the laser shock overlap rate remains unchanged. Then, the low-energy laser shock peening test is carried out on GH4169D, and the residual stress and microstructure are measured.

[0082] Table 2 Low energy laser shock peening process parameters

[0083]

[0084]

[0085] After processing and analyzing the test results, the five undetermined coefficients are as follows:

[0086]

[0087] h s (d,e)=0.24-0.35×d+1.8×e+0.08×d 2 -0.95×e 2 -0.1×d×e (11)

[0088] z d (d,e)=1.28-d-0.67×e+0.36×d 2 +1.3×e 2 -0.58×d×e (12)

[0089] K(d,e)=-170+236×d+221×e-10×d 2 +505×e 2 -462×d×e (13)

[0090] N(d,e)=469-732×d-471×e+81×d 2 +325×e 2 +308×d×e (14)

[0091] Finally, the relationship between the above-mentioned undetermined parameters and the low-energy laser shock strengthening process parameters is substituted into the residual stress prediction model, and the residual stress prediction model corresponding to the residual stress and the process parameters can be obtained.

[0092] In order to verify the accuracy of the model, the model prediction results were compared with the experimental results based on the process parameters (GH4169D) in Table 2, as shown in Figure 3 As shown, Figure 3 (a) The corresponding process parameters are d = 2 mm, e = 0.7 J, Figure 3 (b) The corresponding process parameters are d = 2 mm, e = 0.8 J, Figure 3 (c) The corresponding process parameters are d = 1 mm, e = 0.6 J, Figure 3 (d) The corresponding process parameters are d=1 mm, e=0.8 J. For the above four sets of process parameters, the calculated maximum relative errors are 11.5%, 17.8%, 10.4% and 8.3%, respectively.

[0093] In addition, in order to verify the universality of the residual stress prediction model based on GH4169D to GH4169G and GH4169, the Young’s modulus E and Poisson’s ratio v of GH4169G and GH4169 were introduced into the equation (the linear elastic parameters are shown in Table 2), and the residual stress field was solved in combination.

[0094] Figure 4 The comparison between the residual stress prediction and test results of GH4169G and GH4169 is shown. Figure 4 (a) The process parameters corresponding to GH4169G are d=1mm, e=0.8J, Figure 4 (b) The process parameters corresponding to GH4169G are d=2mm, e=0.8J, Figure 4 (c) The process parameters corresponding to GH4169 are d=1mm, e=0.8J, Figure 4 (d) The process parameters corresponding to GH4169 are d = 2 mm, e = 0.8 J. According to the data in the figure, the relative error between the test value and the calculated value is less than 20%, which proves that the residual stress prediction model based on GH4169D is universal for GH4169 series alloys.

[0095] In another embodiment, according to the above modeling process, the relationship between the undetermined coefficient of the TC17 residual stress prediction model and the process parameters (d=1 mm, e=0.8 J) is as follows:

[0096] A(d,E)=-3.33×d+18.04×E+0.31×d 2 -11.74×E 2 (15)

[0097] h s (d,E)=-0.76×d+3.74×E+0.11×d 2 -2.52×E 2 (16)

[0098] z d (d,E)=-0.71×d+2.29×E+0.29×d 2 -0.69×E 2 -0.69×d×E (17)

[0099] K(d,E)=-383+290×d+934×E-107×d 2 -428×E 2 -140×d×E(18)

[0100] N(d,E)=-36.4-465×d+438×E+5.56×d 2 -400×E 2 +273×d×E (19)

[0101] The Young's modulus and Poisson's ratio of TC17 were introduced into the residual stress field calculation equation, and the residual stress fields were solved when e=0.8J, d=1mm and e=0.8J, d=2mm. Figure 5 shows the comparison between the prediction and the experimental results, where Figure 5 (a) is the residual stress field when e = 0.8 J and d = 1 mm. Figure 5 (b) is the residual stress field when e = 0.8 J and d = 2 mm. Figure 5 Calculation based on the data shows that the prediction relative error of TC17 is less than 15%, which proves that the residual stress prediction model modeling method proposed in the present invention is universal for different alloy materials.

[0102] In summary, the method for calculating the residual stress field of low-energy laser shock strengthening in this embodiment overcomes the problem of the long time required for the existing finite element simulation to calculate the residual stress, and the accuracy is improved relative to the FEM simulation model. Compared with the existing finite element simulation calculation method, the calculation model proposed in the present invention reduces the amount of calculation, thereby shortening the calculation simulation time and improving the calculation efficiency. At the same time, the new calculation model also improves the prediction accuracy, providing a good theoretical basis and technical support for the actual low-energy laser shock strengthening field.

[0103] The present invention also discloses a low-energy laser shock strengthening residual stress field prediction device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor implements the above method when executing the computer program.

[0104] The present invention also discloses an embodiment that provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0105] The present invention also provides a computer program product. When the computer program product runs on a data storage device, the data storage device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0106] If the integrated unit module is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device capable of carrying the computer program code to a storage device, a recording medium, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), an electrical carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0107] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0108] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0109] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the modules or units is only a logical function division, and there may be other division methods in actual implementation, for example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0110] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

Claims

1. A method for predicting residual stress field of low-energy laser shock hardening, characterized in that: The following steps are involved: Generate low-energy laser shock processing simulation data based on process parameters and residual stress measurement results in experimental data; Establishing a residual stress prediction model according to the simulation data; The residual stress prediction model is used to predict the residual stress field of a workpiece subjected to low-energy laser shock strengthening.

2. A method for predicting residual stress field of low-energy laser shock hardening according to claim 1, characterized in that: The simulation data for generating low energy laser shock peening includes: Calibrate the FEM simulation model based on process parameters and residual stress measurement results from the test data; The calibrated FEM simulation model is used to generate simulation data for low-energy laser shock processing.

3. A method for predicting residual stress field of low-energy laser shock hardening according to claim 2, characterized in that: The residual stress prediction model is: Among them, σ x (z) represents the predicted residual stress along the cutting direction at a depth of z on the workpiece, x represents the cutting direction, It represents the stress caused by the inherent strain at depth z on the workpiece along the cutting direction. It represents the restraint reaction force along the cutting direction at the depth z on the workpiece. It represents the stress generated along the cutting direction by the restraint reaction moment M at a depth of z on the workpiece.

4. A method for predicting residual stress field of low-energy laser shock hardening according to claim 3, characterized in that: The method for establishing the residual stress prediction model comprises: Stress calculation formula caused by design inherent strain in, represents the stress caused by the inherent strain at depth z on the workpiece along the cutting direction, v represents the Poisson's ratio of the workpiece material, E is the Young's modulus of the workpiece material, ε x (z) represents the inherent strain along the cutting direction at a depth of z on the workpiece.

5. A method for predicting residual stress field of low-energy laser shock hardening according to claim 4, characterized in that: The calculation method of the inherent strain along the cutting direction at a depth of z on the workpiece is: Where A represents the coefficient of the inherent strain distribution area of ​​the workpiece, h s The coefficient representing the inherent strain layer depth of the workpiece, z d Indicates the depth corresponding to the maximum inherent strain of the workpiece.

6. The method for predicting residual stress field of low-energy laser shock hardening according to claim 3, characterized in that: The method for establishing the residual stress prediction model comprises: The stress calculation formula of the design constraint reaction moment M in, It represents the stress generated by the restraint reaction moment M along the cutting direction at the depth z on the workpiece, N represents the coefficient of the stress generated by the restraint reaction moment M, and h d is the thickness of the workpiece.

7. A method for predicting residual stress field of low-energy laser shock hardening according to any one of claims 3 to 6, characterized in that: Establishing a residual stress prediction model according to the simulation data includes: Taking the simulation data as input, a support vector machine regression method is used to calculate the relationship between the undetermined parameters in the residual stress prediction model and the low-energy laser shock strengthening process parameters; The parameters to be determined include the coefficient A of the inherent strain distribution area of ​​the workpiece, the coefficient h of the inherent strain layer depth, s , the depth z corresponding to the maximum inherent strain of the workpiece d , and the coefficient N of the stress generated by the restraint reaction moment M, is a constant and is not affected by depth.

8. The method for predicting residual stress field of low-energy laser shock hardening according to claim 7, characterized in that: The residual stress prediction model is determined according to the relationship between the unknown parameters and the low-energy laser shock strengthening process parameters.

9. A low-energy laser shock hardening residual stress field prediction device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

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